Maria Lomeli Garcia

About me

I am a research scientist at Babylon Health, UK. Previously, I was a research associate, working with Zoubin Ghahramani at the Machine Learning group, CBL, University of Cambridge and member of Trinity Hall college. I studied my PhD at the Gatsby Unit, UCL, my supervisor was Yee Whye Teh. Before coming to the UK, I did an MSc in Mathematical Sciences at IIMAS, Universidad Nacional Autónoma de México, advised by Ramsés Mena.

Research interests

Journal publications

  • Lomeli, M., Rowland, M., Gretton, A. and Ghahramani, Z., ''Antithetic and Monte Carlo kernel estimators for partial rankings'', Statistics and Computing, 2019 (to appear), available online.
  • Lomeli, M., Favaro, S., Teh, Y. W.,'' A marginal sampler for -Stable Poisson-Kingman mixture models'', Journal of Computational and Graphical Statistics, 2017, Vol 26, 44-53 JCGS.
  • Favaro, S., Lomeli, M., Nipoti, B., Teh, Y.W., ''Stick-breaking representations of -stable Poisson-Kingman models'' , Electronic Journal of Statistics, 2014, Vol. 8, pp 1063-1085 EJS.
  • Favaro, S., Lomeli, M., Teh, Y.W.,''On a class of -stable Poisson-Kingman models and an effective marginalized sampler'', Statistics and Computing, 2014, Vol 25, pp 67-78 StCo.


  • Lomeli, M., Favaro, S.,Teh, Y.W., 2015, ''A hybrid sampler for Poisson-Kingman mixture models'', Neural information Processing Systems NIPS
  • Sejdinovic, D., Strathmann, H., Lomeli Garcia, M., Andrieu, C., Gretton, A., 2014,''Kernel Adaptive Metropolis-Hastings'', International Conference in Machine Learning ICML


  • Bloem-Reddy, B., Mathieu, E., Foster, A., Rainforth, T., Ge, H., Lomeli, M., Ghahramani, Z., Teh, Y.W., 2017, ''Sampling and inference for discrete random probability measures in probabilistic programs'', Approximate Inference workshop, NIPS

    Theses and projects

  • General Bayesian inference schemes in infinite mixture models
    PhD thesis, University College London
    Arxiv version: arXiv:1702.08781
    (Figures 2.2 and 5.1 are not displayed properly, email me for the pdf version)
  • Consistencia Posterior de Modelos Bayesianos No Paramétricos
    (Posterior Consistency of Bayesian Nonparametric Models)
    MSc project, UNAM
    Available upon request (In Spanish)
  • Qué tan "expertos" son los Expertos: Un Modelo de Evaluación y Pronóstico
    Undergraduate thesis, ITAM
    Available upon request (In Spanish)


  • Walecki, R., Buchard, A., Gourgoulias, K., Hart, C., Lomeli, M., Navarro, A. K. W., Zwiessele, M., Johri, S. ''Universal marginaliser for amortised inference and embeddings of generative models'', arXiv preprint.
  • Valera, I., Pradier, M., Lomeli, M. and Ghahramani, Z., ''General Latent Feature Model for Heterogeneous Datasets'', arXiv preprint.


  • June, 2019. Talk at ''Congreso Bayesiano de América Latina'', Peru
  • March, 2019. Talk at the Statistics seminar series, Queen Mary University, London
  • June 11, 2018. Talk at Parallelizing Monte Carlo Algorithms workshop, School of Mathematics, University of Bristol
  • March 15, 2018. Talk at the CamAIML event, Microsoft research Cambridge
  • February 16, 2018. Talk at the University of Glasgow, Statistics seminar
  • Febryary 2, 2018. Talk at UCL, CSML Lunchtime seminar
  • February 1, 2018. Talk at Amazon Cambridge research series seminar
  • August 30, 2017. Talk at the 2017 SMC workshop
  • June 7, 2017. Talk at the ''Congreso Bayesiano de América Latina''
  • June 14, 2016. Talk at the ''Bayes Legacy'' sesssion, 13th ISBA Wold meeting in Sardinia, Italy
  • June 2, 2016. Talk at the Machine Learning group, CBL, University of Cambridge
  • May 5, 2016. Talk at Machine Learning reading group, CBL, University of Cambridge
  • July 16, 2015. Talk at CBL, University of Cambridge
  • June 22, 2015. Talk at the 10th Bayesian Nonparametrics conference
  • June 15, 2015. Talk at the 9th Bayesian Inference for Stochastic Processes conference
  • January 26, 2014. Talk at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México
  • October 24, 2014. Talk at the Computational Statistics seminar, University of Oxford
  • September 24, 2014. Talk at CBL, University of Cambridge
  • March 3, 2014. Talk at the workshop Advances in Scalable Bayesian Computation, available online here


  • Teaching assistant, Part II Statistical modelling course, Statslab, University of Cambridge (Lent, 2018)
  • Coding lab demonstrator, APTS, Statistical computing module for Statistics PhD students, University of Cambridge (December, 2017)
  • Coding lab demonstrator, MLSALT1 graduate course, University of Cambridge (Michaelmas, 2017)
  • Coding lab demonstrator, 3F8 undergraduate course, University of Cambridge (Lent, 2017)
  • Teaching assistant, Statistical Data Mining and Machine Learning MSc in Applied Statistics course, University of Oxford (Hilary term 2014 and 2015)
  • Coding lab demonstrator, Kernel methods module, Introduction to machine learning graduate course, University College London (2013)
  • Teaching assistant, Probabilistic and Unsupervised Learning graduate course, University College London (Autumn, 2012)
  • Lecturer, Stochastic Processes, undergraduate course, Instituto Tecnológico Autónomo de México (Summer, 2011)


  • 2019, Uncertainty in Artificial Intelligence conference
  • 2018, Bayesian Analysis
  • 2018, Journal of Machine Learning Research
  • 2017, Biometrika
  • 2017, Scandinavian Journal of Statistics
  • 2016, Computational Statistics and Data Analysis
  • 2016, Statistics and Computing
  • 2016, 2017, 2019 International Conference in Machine Learning
  • 2013, 2014, 2015, 2017, 2018 Neural Information Processing Systems
  • 2014, 2015, AISTATS


    I was one of the organisers of our CSML Lunch Talk Series.

    Babylon Health
    London, UK